In this section, we present several numerical experiments conducted over the Cora, Citeseer, and Pubmed datasets [1]} with GIST.
Because the datasets in these experiments are small, no wall-clock speedup is observed.
As a result, we exclude speed measurements from this analysis.
All experiments are run for a total of 400 epochs with a step learning rate schedule that decays the learning rate by \(10\times \)  at 50% and 75% of total epochs.
A vanilla GCN model, as described in [2]}, is used.
The model is trained in a full-batch manner using the Adam optimizer [3]}, and all reported results are averaged across five trials with different random seeds.
For all \(L\) -layer models considered, we have that \(d_0\)  and \(d_L\)  are respectively given by the number of features and output classes in the dataset being studied (see Table REF ), whereas the size of every hidden layer \(d_1\)  through \(d_{L-1}\)  is the same, and varies across experiments.
